CN117151231A - Method, device and medium for solving linear system by using variable component sub-line - Google Patents
Method, device and medium for solving linear system by using variable component sub-line Download PDFInfo
- Publication number
- CN117151231A CN117151231A CN202210565232.6A CN202210565232A CN117151231A CN 117151231 A CN117151231 A CN 117151231A CN 202210565232 A CN202210565232 A CN 202210565232A CN 117151231 A CN117151231 A CN 117151231A
- Authority
- CN
- China
- Prior art keywords
- sub
- quantum
- linear system
- distributed
- circuit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000005259 measurement Methods 0.000 claims abstract description 76
- 238000005520 cutting process Methods 0.000 claims description 54
- 238000004364 calculation method Methods 0.000 claims description 39
- 230000006870 function Effects 0.000 claims description 39
- 239000011159 matrix material Substances 0.000 claims description 34
- 238000004590 computer program Methods 0.000 claims description 15
- 238000003860 storage Methods 0.000 claims description 6
- 239000000758 substrate Substances 0.000 claims 1
- 239000002096 quantum dot Substances 0.000 description 14
- 238000004422 calculation algorithm Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 10
- 230000005540 biological transmission Effects 0.000 description 6
- 238000010276 construction Methods 0.000 description 6
- 238000004088 simulation Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000005610 quantum mechanics Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
- G06N10/20—Models of quantum computing, e.g. quantum circuits or universal quantum computers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
- G06N10/60—Quantum algorithms, e.g. based on quantum optimisation, quantum Fourier or Hadamard transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N10/00—Quantum computing, i.e. information processing based on quantum-mechanical phenomena
- G06N10/80—Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computers; Platforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Software Systems (AREA)
- Mathematical Analysis (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Artificial Intelligence (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a device and a medium for solving a linear system by utilizing a variable component sub-line, wherein the method comprises the following steps: firstly, each sub-quantum circuit containing an approximate solution of a linear system to be solved in a cut variable component sub-circuit is obtained, the number of distributed processors to be called is determined according to the number of each sub-quantum circuit, after a call request for the distributed processors is received, each sub-quantum circuit is loaded by the distributed processors to be called, measurement results are obtained by measuring each sub-quantum circuit through the distributed processors respectively, the measurement results of each distributed processor are combined and output, and the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
Description
Technical Field
The invention belongs to the technical field of quantum computing, and particularly relates to a method, a device and a medium for solving a linear system by utilizing a variable component sub-circuit.
Background
The quantum computer is a kind of physical device which performs high-speed mathematical and logical operation, stores and processes quantum information according to the law of quantum mechanics. When a device processes and calculates quantum information and operates on a quantum algorithm, the device is a quantum computer. Quantum computers are a key technology under investigation because of their ability to handle mathematical problems more efficiently than ordinary computers, for example, to accelerate the time to crack RSA keys from hundreds of years to hours.
The quantum computing simulation is a simulation computation which simulates and follows the law of quantum mechanics by means of numerical computation and computer science, and is taken as a simulation program, and the high-speed computing capability of a computer is utilized to characterize the space-time evolution of the quantum state according to the basic law of quantum bits of the quantum mechanics.
Solving linear systems is the core of many scientific and engineering problems, and classical algorithms for solving such problems are collectively known as linear system algorithms. In recent years, a very important result in the field of quantum computing is a quantum linear system algorithm, but the time complexity of solving a linear problem of the algorithm is increased along with the increase of the dimension of an input matrix, so that the solving process of the algorithm may need to call megabytes or even gigabytes of data, the requirement on computing resources is too high, and the actual physical problem cannot be simulated and solved on a common computer, so that the development of quantum computing is limited to a certain extent, thereby causing the application of a user for solving the linear system by utilizing the quantum computing to be not strong and influencing the further expansion of the simulation application of the quantum computing.
Disclosure of Invention
The application aims to provide a method, a device and a medium for solving a linear system by utilizing a variable component sub-line, so as to solve the defects in the prior art, and the method, the device and the medium can provide support for the realization of the variable component sub-line for solving the linear system by utilizing a distributed technology, improve the calculation speed, reduce the depth of the quantum line and promote the further development of the quantum calculation simulation application.
One embodiment of the present application provides a method for solving a linear system using a variable component sub-line, applied to a distributed computing cluster including a main server and a plurality of distributed processors communicatively connected to the main server, the method comprising:
obtaining each sub-quantum circuit formed by cutting a variable component sub-circuit, wherein each sub-quantum circuit comprises an approximate solution of a linear system to be solved;
determining the number of the distributed processors to be called according to the number of each sub-quantum circuit;
after receiving a call request for the distributed processor, loading each sub-quantum circuit by using the distributed processor to be called, and measuring each sub-quantum circuit through the distributed processor to be called to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, and the calculation task corresponds to one sub-quantum circuit which is cut and contains an approximate solution of a linear system to be solved one by one;
And combining and outputting the measurement results of each distributed processor, wherein the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
Alternatively to this, the method may comprise, before the acquisition of the individual sub-circuits formed by the variable component sub-circuit dicing, the method comprises the following steps:
constructing a variable component sub-circuit and obtaining a directed graph corresponding to the variable component sub-circuit, wherein the vertex of the directed graph is used for representing quantum logic gates in the variable component sub-circuit, the edge of the directed graph is used for representing the association relation between the quantum logic gates, and the direction of the edge of the directed graph is used for representing the time sequence relation for executing the quantum logic gates;
and determining the cutting position of the variable component sub-circuit according to the directed graph, and cutting the variable component sub-circuit based on the cutting position.
Optionally, the linear system to be solved includes:
the system of linear equations to be solved ax=b, where a is a coefficient matrix and b is a vector, where the coefficient matrixThe vector b is encoded to obtain |b>＝U b |0>Wherein S is the number of unitary matrices decomposed by a coefficient matrix A, and l is s For the coefficients of the linear system to be solved, the sigma s 、U b Is a unitary matrix.
Optionally, the method further comprises:
constructing a loss function according to the approximate solution, and judging whether the value of the loss function accords with preset precision;
if yes, the approximate solution is used as a target solution of the linear system to be solved, otherwise, the variation parameters in the variation sub-circuit are updated, the approximate solution of the linear system corresponding to the updated variation parameters is obtained, the measurement results of each distributed processor are continuously executed to be combined and output, the obtained distributed measurement results are used as the approximate solution of the linear system to be solved, and until the approximate solution meeting the precision of the value meeting the loss function is obtained and is used as the target solution of the linear system to be solved.
Optionally, the loading the sub-quantum circuits, and measuring the sub-quantum circuits through the distributed processor respectively to obtain a measurement result includes:
and inputting a coefficient matrix A, a vector b and the preset precision of the linear system to be solved, measuring and calculating probability reconstruction of each sub-quantum circuit after cutting is completed through the distributed processor, and taking the obtained result as the value of the loss function.
Optionally, the merging and outputting the measurement result of each distributed processor, where the obtained distributed measurement result is used as an approximate solution of the linear system to be solved, includes:
determining a pre-constructed Hamiltonian amount, and determining an expected value corresponding to the Ha Midu amount according to a measurement result of the distributed processor;
and determining an approximate solution of the linear system to be solved according to the expected value.
Optionally, the loss function is:
wherein the saidAs a loss function, said->As a variation parameter, the I is an identity matrix, theAnd->The U is a parameter-containing sub-logic gate.
Yet another embodiment of the present application provides an apparatus for solving a linear system using a variable component sub-line, the apparatus comprising:
the acquisition module is used for acquiring each sub-quantum circuit formed by cutting the variable component sub-quantum circuit, wherein each sub-quantum circuit comprises an approximate solution of a linear system to be solved;
the determining module is used for determining the number of the distributed processors to be called according to the number of each sub-quantum circuit;
the measuring module is used for loading each sub-quantum circuit by using the distributed processor to be called after receiving a call request for the distributed processor, and measuring each sub-quantum circuit by the distributed processor to be called to obtain a measuring result, wherein each distributed processor is used for simultaneously executing at least one calculation task, and one calculation task corresponds to one sub-quantum circuit which is cut and contains an approximate solution of a linear system to be solved one by one;
And the output module is used for merging and outputting the measurement results of each distributed processor, and the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
Optionally, the apparatus further includes:
the system comprises a construction module, a control module and a control module, wherein the construction module is used for constructing a variable component sub-circuit and acquiring a directed graph corresponding to the variable component sub-circuit, the vertex of the directed graph is used for representing quantum logic gates in the variable component sub-circuit, the edge of the directed graph is used for representing the association relation between the quantum logic gates, and the direction of the edge of the directed graph is used for representing the time sequence relation for executing the quantum logic gates;
and the cutting module is used for determining the cutting position of the variable component sub-circuit according to the directed graph and cutting the variable component sub-circuit based on the cutting position.
Optionally, the apparatus further includes:
the judging module is used for constructing a loss function according to the approximate solution and judging whether the value of the loss function accords with preset precision;
and the updating module is used for taking the approximate solution as the target solution of the linear system to be solved if yes, otherwise, updating the variation parameters in the variation sub-circuit, obtaining the approximate solution of the linear system corresponding to the updated variation parameters, continuously executing the step of combining and outputting the measurement result of each distributed processor, and taking the obtained distributed measurement result as the approximate solution of the linear system to be solved until the approximate solution meeting the precision of the value of the loss function is obtained and is taken as the target solution of the linear system to be solved.
Optionally, the measurement module includes:
the input unit is used for inputting the coefficient matrix A, the vector b and the preset precision of the linear system to be solved, measuring each sub-quantum circuit after cutting is completed through the distributed processor, and carrying out calculation probability reconstruction, wherein the obtained result is used as the value of the loss function.
Optionally, the output module includes:
a first determining unit, configured to determine a pre-constructed hamiltonian amount, and determine an expected value corresponding to the Ha Midu amount according to a measurement result of the distributed processor;
and the second determining unit is used for determining an approximate solution of the linear system to be solved according to the expected value.
An embodiment of the application provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of the above when run.
An embodiment of the application provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the method of any of the above.
Compared with the prior art, the method comprises the steps of firstly obtaining each sub-quantum circuit of the cut variable component sub-circuit, which contains an approximate solution of a linear system to be solved, determining the number of distributed processors to be called according to the number of each sub-quantum circuit, loading each sub-quantum circuit by using the distributed processor to be called after receiving a call request for the distributed processor, and measuring each sub-quantum circuit through the distributed processor to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, combining and outputting the measurement result of each distributed processor, and the obtained distributed measurement result is used as the approximate solution of the linear system to be solved.
Drawings
FIG. 1 is a block diagram of a hardware architecture of a computer terminal for a method of solving a linear system using variable component sub-circuits according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for solving a linear system using variable component sub-circuits according to an embodiment of the present invention;
Fig. 3 is a schematic diagram of a quantum circuit according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a process of cutting a quantum wire into sub-quantum wires according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an original quantum circuit according to an embodiment of the present invention;
fig. 6 is a schematic diagram of two sub-quantum circuits after cutting an original quantum circuit according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an apparatus for solving a linear system using a variable component sub-line according to an embodiment of the present invention.
Detailed Description
The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
The embodiment of the invention firstly provides a method for solving a linear system by utilizing a variable component sub-line, which can be applied to a distributed computing cluster.
A distributed processor running on a computer terminal is described in detail below as an example. Fig. 1 is a block diagram of a hardware architecture of a computer terminal according to a method for solving a linear system using variable component sub-circuits according to an embodiment of the present invention. As shown in fig. 1, the computer terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, and optionally, a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the computer terminal described above. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the method of solving a linear system using variable component sub-circuits in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, i.e., implement the above-described method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 106 is arranged to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
It should be noted that a real quantum computer is a hybrid structure, which includes two major parts: part of the computers are classical computers and are responsible for performing classical computation and control; the other part is quantum equipment, which is responsible for running quantum programs so as to realize quantum computation. The quantum program is a series of instruction sequences written by a quantum language such as the qlunes language and capable of running on a quantum computer, so that the support of quantum logic gate operation is realized, and finally, quantum computing is realized. Specifically, the quantum program is a series of instruction sequences for operating the quantum logic gate according to a certain time sequence.
In practical applications, quantum computing simulations are often required to verify quantum algorithms, quantum applications, etc., due to the development of quantum device hardware. Quantum computing simulation is a process of realizing simulated operation of a quantum program corresponding to a specific problem by means of a virtual architecture (namely a quantum virtual machine) built by resources of a common computer. In general, it is necessary to construct a quantum program corresponding to a specific problem. The quantum program, namely the program for representing the quantum bit and the evolution thereof written in the classical language, wherein the quantum bit, the quantum logic gate and the like related to quantum computation are all represented by corresponding classical codes.
Quantum circuits, which are one embodiment of quantum programs, also weigh sub-logic circuits, are the most commonly used general quantum computing models, representing circuits that operate on qubits under an abstract concept, the composition of which includes qubits, circuits (timelines), and various quantum logic gates, and finally the results often need to be read out by quantum measurement operations.
Unlike conventional circuits, which are connected by metal lines to carry voltage or current signals, in a quantum circuit, the circuit can be seen as being connected by time, i.e., the state of the qubit naturally evolves over time, as indicated by the hamiltonian operator, during which it is operated until a logic gate is encountered.
One quantum program is corresponding to one total quantum circuit, and the quantum program refers to the total quantum circuit, wherein the total number of quantum bits in the total quantum circuit is the same as the total number of quantum bits of the quantum program. It can be understood that: one quantum program may consist of a quantum circuit, a measurement operation for the quantum bits in the quantum circuit, a register to hold the measurement results, and a control flow node (jump instruction), and one quantum circuit may contain several tens to hundreds or even thousands of quantum logic gate operations. The execution process of the quantum program is a process of executing all quantum logic gates according to a certain time sequence. Note that the timing is the time sequence in which a single quantum logic gate is executed.
It should be noted that in classical computation, the most basic unit is a bit, and the most basic control mode is a logic gate, and the purpose of the control circuit can be achieved by a combination of logic gates. Similarly, the way in which the qubits are handled is a quantum logic gate. Quantum logic gates are used, which are the basis for forming quantum circuits, and include single-bit quantum logic gates, such as Hadamard gates (H gates, ada Ma Men), bery-X gates (X gates), bery-Y gates (Y gates), bery-Z gates (Z gates), RX gates, RY gates, RZ gates, and the like; multi-bit quantum logic gates such as CNOT gates, CR gates, iSWAP gates, toffoli gates, and the like. Quantum logic gates are typically represented using unitary matrices, which are not only in matrix form, but also an operation and transformation. The effect of a general quantum logic gate on a quantum state is calculated by multiplying the unitary matrix by the matrix corresponding to the right vector of the quantum state.
The quantum states, i.e. the logic states of the qubits, are represented in the quantum algorithm (or weighing sub-program) in binary, e.g. a group of qubits q0, q1, q2, representing the 0-th, 1-th, 2-th qubits, ordered from high to low as q2q1q0, the quantum states corresponding to the group of qubits being a superposition of the eigenstates corresponding to the group of qubits, the eigenstates corresponding to the group of qubits having a total number of 2 qubits to the power of 8 eigenstates (determined state): the bits of each eigenstate are corresponding to the qubits, i 000>, i001 >, i010 >, i011 >, i100 >, i101 >, i110 >, i111 >, for example, the bits of 000 correspond to q2q1q0 from high to low in the state of i 000> and are dirac symbols.
Described in terms of a single qubit, the logic state of the single qubitMay be at |0>State, |1>State, |0>State sum |1>The superimposed state (uncertain state) of states, which can be expressed in particular as +.>Where c and d are complex numbers representing the quantum state amplitude (probability amplitude), the square of the amplitude modulus |c| 2 And |d| 2 Respectively represent |0>State, |1>Probability of state, |c| 2 +|d| 2 =1. In short, a quantum state is an superposition of eigenstates, when the probability of the other eigenstates is 0, i.e. in a uniquely defined eigenstate.
The idea of distributed computing is now well-known in our life, and for the explanation of "distributed computing" it is generally described as a framework of digital computing that utilizes processors or computers to solve pending computing tasks. Although these processors or computers are physically separate, they cooperate closely in a distributed effort, and small processors and desktop computers for personal use can be integrated in addition to the high performance supercomputers or computers for scientific researchers. In short, distributed computing is a combination of task allocation and coordinated interactions with the goal of making task management as efficient as possible and finding a practically flexible solution. In distributed computing, the computation begins with a special problem-solving strategy, a single problem is split, each part is processed by a computing unit, and distributed application processing operations running on all processors in the computer network are performed.
The application introduces the thought of the distributed computation into the solution of the linear system by utilizing the variable component sub-line to calculate the target solution of the linear system, thereby reducing the calculation time and optimizing the sub-line.
Referring to fig. 2, fig. 2 is a flow chart of a method for solving a linear system by using a variable component sub-line according to an embodiment of the present application.
The present embodiment provides an embodiment of a method for solving a linear system using a variable component sub-line, the method being applied to a distributed computing cluster including a main server and a plurality of distributed processors communicatively connected to the main server, including:
s201: and obtaining each sub-quantum circuit formed by cutting the variable component sub-circuit, wherein each sub-quantum circuit comprises an approximate solution of the linear system to be solved.
Specifically, the linear system to be solved includes:
the system of linear equations to be solved ax=b, where a is a coefficient matrix and b is a vector, where the coefficient matrixThe vector b is encoded to obtain |b>＝U b |0>Wherein S is unitary matrix number decomposed by coefficient matrix ANumber, the l s For the coefficients of the linear system to be solved, the sigma s 、U b Is a unitary matrix.
Before the cut variable sub-line is measured, information of a linear equation set may be input to the variable sub-line, wherein one of the information is a linear combination of S unitary matrices decomposed by the matrix a, so as to encode the matrix a into the sub-line. Here, a may be expressed as:wherein l s Coefficients, sigma, being linear combinations s Is a unitary matrix (unitary operator); another information of inputting linear equation set to variable component sub-line is unitary matrix U obtained by vector b coding b Unitary matrix U b For preparing a quantum state |b proportional to vector b>The method comprises the following steps: normalizing the vector b and encoding it into the quantum wire in the form of |b > = U b I0 >. The solution of the system of linear equations is expressed as +.>
It should be noted that the design may be HEA (Hardware Efficient Ansatz, hardware efficient design), in which HEA lines of each layer are composed of a parameter-containing sub-logic gate (e.g. RY quantum logic gate) and a CNOT quantum logic gate, and the variation parameter is expressed as a rotation angleIs a vector of (a). The method is composed of a single quantum rotating connecting layer and a global entanglement layer, along with the deepening of the layer number, the expression capacity of a line is continuously improved, meanwhile, the training difficulty of the line is increased, the number of quantum bits and the layer number to be set can be determined by the dimension of a linear equation set to be solved, and under the condition of sufficient computing resources, the solution precision can be ensured by the sufficient number of quantum bits and HEA with sufficient layer number to be set.
Before each sub-line formed by variable component sub-line dicing is acquired, the method may include:
step 1: constructing a variable component sub-circuit and obtaining a directed graph corresponding to the variable component sub-circuit, wherein the vertex of the directed graph is used for representing quantum logic gates in the variable component sub-circuit, the edge of the directed graph is used for representing the association relation between the quantum logic gates, and the direction of the edge of the directed graph is used for representing the time sequence relation for executing the quantum logic gates.
Specifically, the vertex of the directed graph is at least used for representing the quantum logic gate in the quantum circuit, and the existence of the single quantum logic gate does not affect the number of quantum bits used by the sub-quantum circuit, so when the directed graph of the quantum circuit is drawn, the single quantum logic gate can be deleted and the position information of the single quantum logic gate is recorded, and execution is resumed after the sub-quantum circuit cutting position is obtained.
For example, a schematic diagram of a quantum circuit shown in fig. 3 is obtained first, where the vertex of the directed graph includes one edge and two points, where one edge is used to represent a quantum logic gate, and two points are used to represent two qubits that act on the quantum logic gate.
Step 2: and determining the cutting position of the variable component sub-circuit according to the directed graph, and cutting the variable component sub-circuit based on the cutting position.
Further, the vertexes of the sub-directed graph of the directed graph can be determined by pre-configuring the number of vertexes of the sub-directed graph, or determining the vertexes of the sub-directed graph of the directed graph through a greedy algorithm, or determining the cutting positions of the directed graph according to the calculation resources of the computing equipment.
For example, a directed graph of a quantum circuit is obtained, the directed graph showing connection relationships between quantum logic gates in the quantum circuit as compared to a more direct visual representation of the quantum circuit; secondly, determining vertexes of sub-directed graphs of the directed graph, determining cutting positions of the directed graph based on the vertexes of the sub-directed graph, and determining the vertexes of the sub-directed graph through connection relation more conveniently and quickly so as to determine the cutting positions of the directed graph through the vertexes of the sub-directed graph; and finally, determining the corresponding cutting point of the cutting position on the quantum circuit, and cutting the quantum circuit based on the cutting position, so that the determination of the cutting position in the quantum circuit is realized when the quantum circuit with more quantum bits is cut into the quantum circuit.
As shown in fig. 4, fig. 4 is a schematic diagram of a process of cutting a quantum wire into sub-quantum wires according to an embodiment of the present invention, and it should be noted that, because wires of variable component quantum wires are relatively complex, the foregoing description of the cutting process is performed by taking a simple quantum wire as an example, and includes conversion of the quantum wire into a directed graph, finding of a cutting position, and reconstruction of a calculation probability based on the cut wire.
According to the theory of line cutting, if the cutting position is observed quantity, the measuring base is M i E { I, X, Y, Z }, if the place is in an initial state, the initial states need to be initialized to the following four initial states q respectively i ∈{|0＞,|1>,|+>(i >) where ++ >, i>In the superimposed state, the two are respectively:this is because for any 2X2 matrix a', there is:
wherein A 'is' 1 ＝Tr(A′ 1 I)[|0><0|+|1><1|]，A′ 2 ＝Tr(A′ 1 Z)[|0><0|-|1><1|]，A′ 3 ＝Tr(A′ 1 X)[2|+><+|-|0><0|-|1><1|]，A′ 4 ＝Tr(A′ 1 Y)[2|i><i|-|0><0|-|1><1|]. The operators in the above formula correspond to the operation of measuring the bit by using the corresponding Pauli base, and the characteristic states in the density matrix in the above formula correspond to the initial states of the quantum bit, and the projection measuring lines of the I and Z gates in the Pauli base are consistent, so that the two can only need to be carried outAnd (5) measuring once. The following will provide a simple quantum circuit cutting example and a probability reconstruction algorithm, and the conventional method for processing the cut quantum circuit to reconstruct the probability is to measure all the quantum bits of the cut sub-quantum circuit and reconstruct all the results to obtain the calculation result of the original quantum circuit. However, for the original quantum circuit described below, the method is just to measure the calculation result of one quantum bit, and the whole measurement is not required. A calculation method for only one qubit measurement is therefore proposed for this case.
Referring to fig. 5, an original quantum circuit schematic diagram provided by the embodiment of the present invention is shown in fig. 5, where black dots and # -icons in the figure represent a CNOT quantum logic gate, where the black dots are on a control bit of the CNOT quantum logic gate, on a target bit of the CNOT quantum logic gate, and measurement is required for a third quantum bit in the quantum circuit. When the quantum wire shown in fig. 5 is cut, the quantum wire may be cut into two sub-quantum wires (1) and (2) as shown in fig. 6 according to the same wire cutting method as described above. For the cut sub-quantum circuit (1), the first quantum bit needs to be measured, and projection measurement needs to be performed by using Pauli groups (I, X and Y) during measurement, so that the calculation result of the cut sub-quantum circuit (1) shown in the table 1 is obtained:
table 1: calculation result table of sub-quantum circuit (1) after cutting
| Measuring base | Probability P (0) | Probability P (1) |
| I | 0.75 | 0.25 |
| X | 0.933013 | 0.0669873 |
| Y | 0.5 | 0.5 |
Meanwhile, for the cut sub-quantum circuit (2), after the first quantum bit needs to be initialized into |0>, |1>, |+>, and|i >, the quantum circuit is operated, and the second quantum bit is measured, so that a measurement result of the sub-quantum circuit can be obtained as shown in table 2:
Table 2: calculation result table of sub-quantum circuit (2) after cutting
| Initial state | Probability P (0) | Probability P (1) |
| |0> | 0.5 | 0.5 |
| |1> | 0.5 | 0.5 |
| |+> | 1.0 | 0.0 |
| |i> | 0.5 | 0.5 |
At this time, the probability value of 0 of the third qubit on the original quline as shown in fig. 5 can be reconstructed by using the following calculation formula, namely:
substituting the data in the table above can result in: p (P) 1 ＝(1.5,0.5,0.866026,0) T ，P 2 ＝(0.5,0.5,1,0) T Thus:
and since the operation result of the original quantum circuit is P (0) = 0.933013, the operation result is consistent with the calculation result after cutting.
It should be noted that, the single cut point formula may be generalized to the multi-cut point formula, and two cut points are exemplified, that is, for any matrix of 4X 4:
above-mentionedσ i Is obtained by developing corresponding Pauli base with sigma 4 By way of example only, at this time, P 1 There are 16 items in total, namely:
correspondingly, P 2 There are also 16 items, namely:
the calculation formula of the measured quantum bit P (0) in the original quantum circuit is finally obtained as follows:
s202: and determining the number of the distributed processors to be called according to the number of each sub-quantum circuit.
Illustratively, for the original quantum circuit as shown in fig. 5, there are two sub-quantum circuits after dicing, and in general, there should be 2 distributed processors to be invoked. For complex original quantum circuits, tens or hundreds of sub-quantum circuits after cutting are possible, and the traditional method is very time-consuming and has no advantages in the measurement process, so that the number of distributed processors to be called is determined through the number of each sub-quantum circuit, at the moment, the circuit depth can be greatly reduced by using the thought of distributed computation, the new thought is provided for a complex computing system when computation is reduced, and the difficulty is reduced for simulating variable component sub-circuits on a real quantum chip.
S203: after receiving a call request for the distributed processor, loading each sub-quantum circuit by using the distributed processor to be called, and measuring each sub-quantum circuit through the distributed processor to be called to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, and one calculation task corresponds to one sub-quantum circuit which is cut and contains an approximate solution of a linear system to be solved one by one.
Specifically, loading each sub-quantum circuit, and measuring each sub-quantum circuit through the distributed processor to obtain a measurement result, which may include:
and inputting a coefficient matrix A, a vector b and the preset precision of the linear system to be solved, measuring each sub-quantum line after cutting is completed through the distributed processor, and carrying out calculation probability reconstruction, wherein the obtained result is used as the value of the loss function.
After receiving a call request for a required distributed processor, each distributed processor loads at least one cut sub-quantum circuit containing an approximate solution of a linear system to be solved, and measures each sub-quantum circuit through the distributed processor to obtain a measurement result.
S204: and combining and outputting the measurement results of each distributed processor, wherein the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
Specifically, the merging and outputting the measurement result of each distributed processor, where the obtained distributed measurement result is used as an approximate solution of the linear system to be solved, may include:
determining a pre-constructed Hamiltonian amount, and determining an expected value corresponding to the Ha Midu amount according to a measurement result of the distributed processor;
and determining an approximate solution of the linear system to be solved according to the expected value.
Specifically, after the measurement results of each distributed processor are combined and output, the final state is obtainedFor reading the quantum state information, a pre-constructed hamiltonian amount can be used>Measuring the final state to obtain the value of the approximate solution construction loss function of the linear system>The key to this process is the pre-constructed hamiltonian +.>Will expect the valueA value is determined that constructs a loss function for the approximate solution.
When the value of the loss function goes to 0, thenIs a solution of a linear system, and is converted into a classical vector to be expressed asNotably, the quantum state output by the quantum wire is normalized, since the vector b encoded into the quantum wire is the normalized quantum state |b >. Assume the solution of the system of equations derived from quantum wires +.>Solution to the true linear system>The ratio relation is as follows:
substituting the method into a linear system to be solved to obtain the following steps:
the transposes of the above are multiplied by the left and right sides respectively, and the following can be obtained:
the left end term in the aboveEqual to +.>The term can therefore be solved by solving the coefficient η, resulting in a solution for the linear system.
In the above steps, the approximate solution of the linear system is solved by combining the distributed method with the variable component sub-line, but the accuracy of the approximate solution is poor, and the target solution needs to be solved by further using the iterative idea, so as to improve the calculation accuracy.
And constructing a loss function according to the approximate solution, and judging whether the value of the loss function accords with the preset precision.
Wherein the loss function is:
wherein the saidAs a loss function, said->As a variation parameter, the I is an identity matrix, theAnd->The U is a parameter-containing sub-logic gate.
In the partial derivative form of the loss function described above, it can be divided into three terms, namely: first partial guide itemSecond partial guide item->And third partial guide item->These three terms can be obtained by measurement operations, respectively, specifically:
/>
And due to:
to meet the measurement needs, willIs rewritten into the following form:
wherein,is a unitary matrix.
The quantum circuits for solving the value of the loss function may be all solved by cutting the circuits and combining the distributed computing method.
Judging whether the value of the loss function accords with the precision or not, specifically:
according to the approximate solution of the linear system to be solved, the target solution of the linear system to be solved is further solved, mainly by utilizing the pre-selected Hamiltonian quantityWhen acting on the final quantum state, the value of the approximate solution construction loss function of the linear system to be solved in the current step can be obtained, and whether the value of the loss function accords with the precision can be further judged, wherein the precision can be set by a user according to the calculation requirement, for example, 10 is taken -6 Or 0.
If yes, the approximate solution is used as a target solution of the linear system to be solved, otherwise, the variation parameters in the variation sub-circuit are updated, the approximate solution of the linear system corresponding to the updated variation parameters is obtained, the measurement results of each distributed processor are continuously executed to be combined and output, the obtained distributed measurement results are used as the approximate solution of the linear system to be solved, and until the approximate solution meeting the precision of the value meeting the loss function is obtained and is used as the target solution of the linear system to be solved.
Specifically, if the value of the loss function of the current step constructed according to the approximate solution accords with the preset precision, the obtained approximate solution is exactly the target solution of the linear system to be solved; otherwise, updating the variation parameters in the variation sub-circuit through an optimization algorithm.
For example, using a conventional optimization method, gradient descent method, the variation parameters are updated by the following equation
Wherein k is an integer not less than 1, beta is a learning rate,gradient of the loss function versus θ.
And then, transmitting the updated variation parameters to the cut variation sub-line, continuously executing evolution and measurement of the steps, updating the approximate solution and solving the loss function by continuously iterating the variation parameters until a predicted solution meeting the accuracy of the value of the loss function is obtained and is used as a target solution of the linear system to be solved.
It can be seen that the method includes the steps of firstly obtaining each sub-quantum circuit of the cut variable component sub-circuit, which contains an approximate solution of the linear system to be solved, determining the number of distributed processors to be called according to the number of each sub-quantum circuit, loading each sub-quantum circuit by using the distributed processor to be called after receiving a call request for the distributed processor, and measuring each sub-quantum circuit by using the distributed processor to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, merging and outputting the measurement result of each distributed processor, and the obtained distributed measurement result is used as the approximate solution of the linear system to be solved.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an apparatus for solving a linear system by using a variable component sub-line according to an embodiment of the present invention, corresponding to the flow shown in fig. 2, the apparatus includes:
an obtaining module 701, configured to obtain each sub-quantum circuit formed by cutting a variable component sub-circuit, where each sub-quantum circuit includes an approximate solution of a linear system to be solved;
a determining module 702, configured to determine, according to the number of the sub-quantum circuits, the number of the distributed processors to be invoked;
the measurement module 703 is configured to load each sub-quantum circuit by using the distributed processor to be invoked after receiving a invocation request for the distributed processor, and measure each sub-quantum circuit by using the distributed processor to be invoked to obtain a measurement result, where each distributed processor is configured to simultaneously perform at least one calculation task, and the one calculation task corresponds to one sub-quantum circuit after cutting, where the sub-quantum circuit includes an approximate solution of a linear system to be solved;
and the output module 704 is configured to combine and output the measurement results of each of the distributed processors, where the obtained distributed measurement results are used as an approximate solution of the linear system to be solved.
Specifically, the device further comprises:
the system comprises a construction module, a control module and a control module, wherein the construction module is used for constructing a variable component sub-circuit and acquiring a directed graph corresponding to the variable component sub-circuit, the vertex of the directed graph is used for representing quantum logic gates in the variable component sub-circuit, the edge of the directed graph is used for representing the association relation between the quantum logic gates, and the direction of the edge of the directed graph is used for representing the time sequence relation for executing the quantum logic gates;
and the cutting module is used for determining the cutting position of the variable component sub-circuit according to the directed graph and cutting the variable component sub-circuit based on the cutting position.
Specifically, the device further comprises:
the judging module is used for constructing a loss function according to the approximate solution and judging whether the value of the loss function accords with preset precision;
and the updating module is used for taking the approximate solution as the target solution of the linear system to be solved if yes, otherwise, updating the variation parameters in the variation sub-circuit, obtaining the approximate solution of the linear system corresponding to the updated variation parameters, continuously executing the step of combining and outputting the measurement result of each distributed processor, and taking the obtained distributed measurement result as the approximate solution of the linear system to be solved until the approximate solution meeting the precision of the value of the loss function is obtained and is taken as the target solution of the linear system to be solved.
Specifically, the measurement module includes:
the input unit is used for inputting the coefficient matrix A, the vector b and the preset precision of the linear system to be solved, measuring each sub-quantum circuit after cutting is completed through the distributed processor, and carrying out calculation probability reconstruction, wherein the obtained result is used as the value of the loss function.
Specifically, the output module includes:
a first determining unit, configured to determine a pre-constructed hamiltonian amount, and determine an expected value corresponding to the Ha Midu amount according to a measurement result of the distributed processor;
and the second determining unit is used for determining an approximate solution of the linear system to be solved according to the expected value.
Compared with the prior art, the method comprises the steps of firstly obtaining each sub-quantum circuit of the cut variable component sub-circuit, which contains an approximate solution of a linear system to be solved, determining the number of distributed processors to be called according to the number of each sub-quantum circuit, loading each sub-quantum circuit by using the distributed processor to be called after receiving a call request for the distributed processor, and measuring each sub-quantum circuit through the distributed processor to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, combining and outputting the measurement result of each distributed processor, and the obtained distributed measurement result is used as the approximate solution of the linear system to be solved.
The embodiment of the invention also provides a storage medium, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the method embodiments described above when run.
Specifically, in the present embodiment, the above-described storage medium may be configured to store a computer program for executing the steps of:
s201: obtaining each sub-quantum circuit formed by cutting a variable component sub-circuit, wherein each sub-quantum circuit comprises an approximate solution of a linear system to be solved;
s202: determining the number of the distributed processors to be called according to the number of each sub-quantum circuit;
s203: after receiving a call request for the distributed processor, loading each sub-quantum circuit by using the distributed processor to be called, and measuring each sub-quantum circuit through the distributed processor to be called to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, and the calculation task corresponds to one sub-quantum circuit which is cut and contains an approximate solution of a linear system to be solved one by one;
S204: and combining and outputting the measurement results of each distributed processor, wherein the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
Specifically, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
An embodiment of the invention also provides an electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the steps of any of the method embodiments described above.
Specifically, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Specifically, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s201: obtaining each sub-quantum circuit formed by cutting a variable component sub-circuit, wherein each sub-quantum circuit comprises an approximate solution of a linear system to be solved;
S202: determining the number of the distributed processors to be called according to the number of each sub-quantum circuit;
s203: after receiving a call request for the distributed processor, loading each sub-quantum circuit by using the distributed processor to be called, and measuring each sub-quantum circuit through the distributed processor to be called to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, and the calculation task corresponds to one sub-quantum circuit which is cut and contains an approximate solution of a linear system to be solved one by one;
s204: and combining and outputting the measurement results of each distributed processor, wherein the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (10)
1. A method of solving a linear system using a variable component sub-line, applied to a distributed computing cluster including a main server and a plurality of distributed processors communicatively coupled to the main server, the method comprising:
obtaining each sub-quantum circuit formed by cutting a variable component sub-circuit, wherein each sub-quantum circuit comprises an approximate solution of a linear system to be solved;
determining the number of the distributed processors to be called according to the number of each sub-quantum circuit;
after receiving a call request for the distributed processor, loading each sub-quantum circuit by using the distributed processor to be called, and measuring each sub-quantum circuit through the distributed processor to be called to obtain a measurement result, wherein each distributed processor is used for simultaneously executing at least one calculation task, and the calculation task corresponds to one sub-quantum circuit which is cut and contains an approximate solution of a linear system to be solved one by one;
and combining and outputting the measurement results of each distributed processor, wherein the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises, before the acquisition of the individual sub-circuits formed by the variable component sub-circuit dicing, the method comprises the following steps:
constructing a variable component sub-circuit and obtaining a directed graph corresponding to the variable component sub-circuit, wherein the vertex of the directed graph is used for representing quantum logic gates in the variable component sub-circuit, the edge of the directed graph is used for representing the association relation between the quantum logic gates, and the direction of the edge of the directed graph is used for representing the time sequence relation for executing the quantum logic gates;
and determining the cutting position of the variable component sub-circuit according to the directed graph, and cutting the variable component sub-circuit based on the cutting position.
3. The method of claim 1, wherein the linear system to be solved comprises:
the system of linear equations to be solved ax=b, where a is a coefficient matrix and b is a vector, where the coefficient matrixThe vector b is encoded to obtain |b>＝U b |0>Wherein S is the number of unitary matrices decomposed by a coefficient matrix A, and l is s For the coefficients of the linear system to be solved, the sigma s 、U b Is a unitary matrix.
4. A method according to claim 3, characterized in that the method further comprises:
Constructing a loss function according to the approximate solution, and judging whether the value of the loss function accords with preset precision;
if yes, the approximate solution is used as a target solution of the linear system to be solved, otherwise, the variation parameters in the variation sub-circuit are updated, the approximate solution of the linear system corresponding to the updated variation parameters is obtained, the measurement results of each distributed processor are continuously executed to be combined and output, the obtained distributed measurement results are used as the approximate solution of the linear system to be solved, and until the approximate solution meeting the precision of the value meeting the loss function is obtained and is used as the target solution of the linear system to be solved.
5. The method of claim 4, wherein loading the sub-quantum wires and measuring the sub-quantum wires by the distributed processor, respectively, to obtain a measurement result comprises:
and inputting a coefficient matrix A, a vector b and the preset precision of the linear system to be solved, measuring and calculating probability reconstruction of each sub-quantum circuit after cutting is completed through the distributed processor, and taking the obtained result as the value of the loss function.
6. The method of claim 5, wherein the combining and outputting the measurement results of each of the distributed processors, the obtained distributed measurement results being an approximate solution of the linear system to be solved, comprises:
determining a pre-constructed Hamiltonian amount, and determining an expected value corresponding to the Ha Midu amount according to a measurement result of the distributed processor;
and determining an approximate solution of the linear system to be solved according to the expected value.
7. The method of claim 4, wherein the loss function is:
wherein the saidAs a loss function, said->As a variation parameter, the I is an identity matrix, theAnd->The U is a parameter-containing sub-logic gate.
8. An apparatus for solving a linear system using variable component sub-lines, applied to a distributed computing cluster, the distributed computing cluster comprising a main server and a plurality of distributed processors communicatively coupled to the main server, the apparatus comprising:
the acquisition module is used for acquiring each sub-quantum circuit formed by cutting the variable component sub-quantum circuit, wherein each sub-quantum circuit comprises an approximate solution of a linear system to be solved;
The determining module is used for determining the number of the distributed processors to be called according to the number of each sub-quantum circuit;
the measuring module is used for loading each sub-quantum circuit by using the distributed processor to be called after receiving a call request for the distributed processor, and measuring each sub-quantum circuit by the distributed processor to be called to obtain a measuring result, wherein each distributed processor is used for simultaneously executing at least one calculation task, and one calculation task corresponds to one sub-quantum circuit which is cut and contains an approximate solution of a linear system to be solved one by one;
and the output module is used for merging and outputting the measurement results of each distributed processor, and the obtained distributed measurement results are used as the approximate solution of the linear system to be solved.
9. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 1 to 7 when run.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of the claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210565232.6A CN117151231A (en) | 2022-05-23 | 2022-05-23 | Method, device and medium for solving linear system by using variable component sub-line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210565232.6A CN117151231A (en) | 2022-05-23 | 2022-05-23 | Method, device and medium for solving linear system by using variable component sub-line |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117151231A true CN117151231A (en) | 2023-12-01 |
Family
ID=88899370
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210565232.6A Pending CN117151231A (en) | 2022-05-23 | 2022-05-23 | Method, device and medium for solving linear system by using variable component sub-line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117151231A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118014092A (en) * | 2024-04-08 | 2024-05-10 | 国开启科量子技术(安徽)有限公司 | Quantum computing method, quantum circuit, device and medium for determining function classification |
| CN118014092B (en) * | 2024-04-08 | 2024-06-28 | 国开启科量子技术(安徽)有限公司 | Quantum computing method, quantum circuit, device and medium for determining function classification |
-
2022
- 2022-05-23 CN CN202210565232.6A patent/CN117151231A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118014092A (en) * | 2024-04-08 | 2024-05-10 | 国开启科量子技术(安徽)有限公司 | Quantum computing method, quantum circuit, device and medium for determining function classification |
| CN118014092B (en) * | 2024-04-08 | 2024-06-28 | 国开启科量子技术(安徽)有限公司 | Quantum computing method, quantum circuit, device and medium for determining function classification |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112232513B (en) | Quantum state preparation method and device | |
| CN113850389B (en) | Quantum circuit construction method and device | |
| CN114492814B (en) | Method, device and medium for calculating energy of simulation target system based on quanta | |
| CN114492815B (en) | Method, device and medium for calculating target system energy based on quantum chemistry | |
| CN114358216B (en) | Quantum clustering method based on machine learning framework and related device | |
| CN114819168B (en) | Quantum comparison method and device for matrix eigenvalues | |
| CN116011682B (en) | Meteorological data prediction method and device, storage medium and electronic device | |
| CN116011681A (en) | Meteorological data prediction method and device, storage medium and electronic device | |
| CN113128015A (en) | Method and system for predicting resources required by single-amplitude analog quantum computation | |
| CN114819163B (en) | Training method and device for quantum generation countermeasure network, medium and electronic device | |
| CN114511094B (en) | Quantum algorithm optimization method and device, storage medium and electronic device | |
| CN114519429B (en) | Method, device and medium for obtaining observability quantity of target system | |
| CN115809707B (en) | Quantum comparison operation method, device, electronic device and basic arithmetic component | |
| CN117151231A (en) | Method, device and medium for solving linear system by using variable component sub-line | |
| CN116400430B (en) | Meteorological data prediction method and device, storage medium and electronic device | |
| CN116263883A (en) | Quantum linear solving method, device and equipment based on polynomial preprocessor | |
| CN115713122B (en) | Method and device for determining size relation between quantum data and classical data | |
| CN115879557B (en) | Quantum circuit-based data size comparison method and device and quantum computer | |
| CN115879558B (en) | Method and device for comparing sizes of multiple quantum states | |
| CN116431807B (en) | Text classification method and device, storage medium and electronic device | |
| CN115879559B (en) | Method and device for judging equivalence relation among multiple quantum states and quantum computer | |
| CN114970872B (en) | Quantum circuit compiling method and device, medium and electronic device | |
| CN117056646A (en) | Variable component sub-linear solving method, device and medium for subspace | |
| CN117951429A (en) | Partial differential equation set solving method and device | |
| CN116167407A (en) | Quantum circulation neural network-based data prediction method and related equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |